In the business world, it is difficult to replace attriting associates with resources from other segment of resource pool. This is due to specific nature of services being offered; for example an IT industry may typically involve data centers, storage, security, networking and other infrastructure support when compared to application or development support that may be more specific to another service industry. In a given situation, due to increasingly specialized customer requirements, the highly talented individuals (or for that matter any experienced professionals) who attrite can only be replaced by external hiring or by specialized cross training. External hiring involves an average of 85 days of replacement time, not to mention the hiring and induction/shadow costs for replacements. Thus retention is of paramount importance to any organization, and hence critical need for an attrition risk analysis system is highlighted.
Voluntary employee attrition in an enterprise for any reason whatsoever, affects project delivery schedules and business operations continuity. Proactively identifying which employees are likely to voluntarily attrite at any given point in time helps HR personnel to quickly and proactively estimate what retention strategies could be adopted to retain these employees successfully. It also aids in timely replacement planning.
Conventional attrition prediction models use machine learning methods with numerous attrition triggers as input datasets. These inputs are then internally multiplied with weights that are self-adjusted via error backpropagation by the machine learning model, and the likelihood of attrition is predicted as the classified output. This training is done via initially supervised learning methods. Employees are then discretely categorized on the basis of their probability to attrite, derived from discrete thresholds on the output that is the cumulative sum of the weighted inputs and in some cases normalized via an activation function i.e, the employees are scored cumulatively on multiple risk parameters and a cut off value is applied on the cumulative score to predict the employee's attrition risk. This type of cumulative summing and thresholds does not help distinguish who are the most likely and the least likely risks category.